On the Role of Theory and Modeling in Neuroscience

References (200)

1. D. Marr, From the Retina to the Neocortex: Selected Papers of David Marr (Edited by L. M. Vaina. Birkhäuser, 1991).
2. A. L. Hodgkin, A. F. Huxley, A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117 , 500-544 (1952).
3. J. O'Keefe, L. Nadel, The Hippocampus as a Cognitive Map (Clarendon Press, 1978).
4. R. E. Goldstein, Are theoretical results "Results"? Elife 7 (2018).
5. W. Bialek, Perspectives on theory at the interface of physics and biology. Rep. Prog. Phys. 81 , 012601 (2018).
6. R. Phillips, Theory in Biology: Figure 1 or Figure 7? Trends Cell Biol. 25 , 723-729 (2015).
7. K. R. Popper, The logic of scientific discovery New York. Science (1959).
8. T. S. Kuhn, The Structure of Scientific Revolutions: 50th Anniversary Edition (University of Chicago Press, 2012).
9. I. Lakatos, Science and pseudoscience. Philosophical papers 1 , 1-7 (1978).
10. S. Firestein, Failure: Why Science Is So Successful (Oxford University Press, 2015).
11. P. Godfrey-Smith, An introduction to the philosophy of science: Theory and reality (2003).
12. P. Feyerabend, Against Method (Verso, 1993).
13. S. Firestein, Ignorance: How It Drives Science (Oxford University Press, USA, 2012).
14. M. Ben-Ari, Just A Theory: Exploring The Nature Of Science (Prometheus Books, 2011).
15. B. C. van Fraassen, The Scientific Image (Clarendon Press, 1980).
16. N. David Mermin, What's Wrong with this Pillow? Phys. Today 42 , 9-11 (1989).
17. D. Kaiser, History: Shut up and calculate! Nature 505 , 153-155 (2014).
18. L. Laudan, Progress and Its Problems: Towards a Theory of Scientific Growth (University of California Press, 1978).
19. H. Douglas, Pure science and the problem of progress. Stud. Hist. Philos. Sci. 46 , 55-63 (2014).
20. A. Franklin, Forging, cooking, trimming, and riding on the bandwagon. Am. J. Phys. 52 , 786-793 (1984).
21. M. Jeng, A selected history of expectation bias in physics. Am. J. Phys. 74 , 578-583 (2006).
22. A. D. Redish, E. Kummerfeld, R. L. Morris, A. C. Love, Opinion: Reproducibility failures are essential to scientific inquiry. Proc. Natl. Acad. Sci. U. S. A. 115 , 5042-5046 (2018).
23. A. Newell, H. A. Simon, Human problem solving (Prentice-Hall, 1972).
24. W. Bechtel, R. C. Richardson, Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research (MIT Press, 2010).
25. W. B. Scoville, B. Milner, Loss of recent memory after bilateral hippocampal lesions. J. Neurol. Neurosurg. Psychiatry 20 , 11-21 (1957).
26. L. R. Squire, Memory and Brain (Oxford University Press, 1987). March 27, 2020 preprint Levenstein et al . -On the role of theory and modeling in neuroscience | 18
27. L. Nadel, "Multiple memory systems: What and Why, an update" in Memory Systems 1994 , D. L. Schacter, E. Tulving, Eds. (MIT Press, 1994), pp. 39-64.
28. D. L. Schacter, The Seven Sins of Memory (Houghton Mifflin, 2001).
29. B. W. Balleine, A. Dickinson, Goal-directed instrumental action: contingency and incentive learning and their cortical substrates. Neuropharmacology 37 , 407-419 (1998).
30. N. D. Daw, Y. Niv, P. Dayan, Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nat. Neurosci. 8 , 1704-1711 (2005).
31. A. D. Redish, The Mind within the Brain: How we make decisions and how those decisions go wrong (Oxford, 2013).
32. R. Descartes, Discours de la Méthode Pour bien conduire sa raison, et chercher la vérité dans les sciences (1637).
33. H. Chang, Scientific Progress: Beyond Foundationalism and Coherentism 1. Royal Institute of Philosophy Supplements 61 , 1-20 (2007).
34. H. Chang, The Persistence of Epistemic Objects Through Scientific Change. Erkenntnis 75 , 413-429 (2011).
35. P. Luyten, L. C. Mayes, P. Fonagy, M. Target, S. J. Blatt, Handbook of Psychodynamic Approaches to Psychopathology (Guilford Publications, 2015).
36. American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders (DSM-5®) (American Psychiatric Pub, 2013).
37. B. N. Cuthbert, T. R. Insel, Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Med. 11 , 126 (2013).
38. T. R. Insel, B. N. Cuthbert, Medicine. Brain disorders? Precisely. Science 348 , 499-500 (2015).
39. C. G. Hempel, P. Oppenheim, Studies in the Logic of Explanation. Philos. Sci. 15 , 135-175 (1948).
40. J. Woodward, "Scientific Explanation" in The Stanford Encyclopedia of Philosophy , E. N. Zalta, Ed. (2019).
41. R. G. Winther, The Structure of Scientific Theories. The Stanford Encyclopedia of Philosophy (2016).
42. M. Weisberg, Simulation and Similarity: Using Models to Understand the World (OUP USA, 2013).
43. R. Frigg, S. Hartmann, Models in science (2006).
44. B. Hille, Ion Channels of Excitable Membranes (Sinauer, 2001).
45. E. Marder, Models identify hidden assumptions. Nature Neuroscience 3 , 1198-1198 (2000).
46. J. M. Epstein, Why model? Journal of Artificial Societies and Social Simulation 11 , 12 (2008).
47. J. D. Watson, F. H. Crick, Molecular structure of nucleic acids; a structure for deoxyribose nucleic acid. Nature 171 , 737-738 (1953).
48. U. Alon, An introduction to systems biology: design principles of biological circuits (Chapman and Hall/CRC, 2006).
49. A. D. Dorval, W. M. Grill, Deep brain stimulation of the subthalamic nucleus reestablishes neuronal information transmission in the 6-OHDA rat model of parkinsonism. J. Neurophysiol. 111 , 1949-1959 (2014).
50. W. Schultz, et al. , Deficits in reaction times and movement times as correlates of hypokinesia in monkeys with MPTP-induced striatal dopamine depletion. J. Neurophysiol. 61 , 651-668 (1989).
51. W. Rall, "Cable theory for dendritic neurons" in Methods in Neuronal Modeling , C. Koch, I. Segev, Eds. (MIT Press, 1992), pp. 9-62.
52. W. Gerstner, W. Kistler, Spiking Neuron Models (Cambridge University Press, 2002).
53. J. W. Langston, J. Palfreman, The Case of the Frozen Addicts: How the Solution of a Medical Mystery Revolutionized the Understanding of Parkinson's Disease (IOS Press, 2013).
54. A. Rosenblueth, N. Wiener, The Role of Models in Science. Philos. Sci. 12 , 316-321 (1945).
55. T. Stafford, What use are computational models of cognitive processes? in Connectionist Models of Behaviour and Cognition II: Proceedings of the 11th Neural Computation and Psychology Workshop , Mayor, J., Ruh, N., Plunkett, K, Ed. (World Scientific., 2009).
56. N. Cartwright, Models: The Blueprints for Laws. Philos. Sci. 64 , S292-S303 (1997). March 27, 2020 preprint Levenstein et al . -On the role of theory and modeling in neuroscience | 19
57. C. Koch, I. Segev, Eds., Methods in Neuronal Modeling (MIT Press, 1989).
58. W. Gerstner, H. Sprekeler, G. Deco, Theory and simulation in neuroscience. Science 338 , 60-65 (2012).
59. P. Dayan, L. F. Abbott, Theoretical Neuroscience (MIT Press, 2001).
60. D. M. Kaplan, W. Bechtel, Dynamical models: an alternative or complement to mechanistic explanations? Top. Cogn. Sci. 3 , 438-444 (2011).
61. A. D. Redish, Beyond the Cognitive Map: From Place Cells to Episodic Memory (MIT Press, 1999).
62. L. L. Colgin, Five Decades of Hippocampal Place Cells and EEG Rhythms in Behaving Rats. The Journal of Neuroscience 40 , 54-60 (2020).
63. C. F. Craver, Explaining the Brain: Mechanisms and the Mosaic Unity of Neuroscience (Clarendon Press, 2007).
64. D. M. Kaplan, Explanation and description in computational neuroscience. Synthese 183 , 339 (2011).
65. C. Linnaeus, Systema Naturae (1758).
66. S. R. y. Cajal, N. Swanson, L. W. Swanson, Histology of the nervous system of man and vertebrates (Oxford University Press, 1995).
67. D. Salsburg, The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century (Macmillan, 2001).
68. R. E. Kass, U. T. Eden, E. N. Brown, Analysis of Neural Data (Springer, 2014).
69. M. T. Harrison, A. Amarasingham, R. E. Kass, "Statistical Identification of Synchronous Spiking" in Spike Timing: Mechanisms and Function , P. M. DiLorenzo, J. D. Victor, Eds. (CRC Press, 2013), p. 77.
70. A. Amarasingham, S. Geman, M. T. Harrison, Ambiguity and nonidentifiability in the statistical analysis of neural codes. Proc. Natl. Acad. Sci. U. S. A. 112 , 6455-6460 (2015).
71. M. Baker, 1,500 scientists lift the lid on reproducibility. Nature 533 , 452-454 (2016).
72. S. N. Goodman, D. Fanelli, J. P. A. Ioannidis, What does research reproducibility mean? Sci. Transl. Med. 8 , 341ps12 (2016).
73. D. Fanelli, Opinion: Is science really facing a reproducibility crisis, and do we need it to? Proc. Natl. Acad. Sci. U. S. A. 115 , 2628-2631 (2018).
74. National Academies of Sciences Engineering, Medicine, Reproducibility and Replicability in Science (The National Academies Press, 2019).
75. drugmonkey, Generalization, not "reproducibility." Drugmonkey (2018) (January 5, 2020).
76. P. Smaldino, Better methods can't make up for mediocre theory. Nature 575 , 9 (2019).
77. P. Machamer, L. Darden, C. F. Craver, Thinking about Mechanisms. Philos. Sci. 67 , 1-25 (2000).
78. A. D. Redish, R. Kazinka, A. B. Herman, Taking an engineer's view: Implications of network analysis for computational psychiatry. Behav. Brain Sci. 42 , e24 (2019).
79. L. Elefteriadou, An Introduction to Traffic Flow Theory (Springer, New York, NY, 2014).
80. M. S. Gazzaniga, Whos in charge. Free will and the science of the brain. New York: Ecco (2011).
81. S. P. Ellner, J. Guckenheimer, Dynamic Models in Biology (Princeton University Press, 2006).
82. E. M. Izhikevich, Dynamical Systems in Neuroscience (MIT Press, 2007).
83. G. Bard Ermentrout, D. H. Terman, Mathematical Foundations of Neuroscience (Springer Science & Business Media, 2010).
84. F. Gabbiani, S. J. Cox, Mathematics for Neuroscientists (Academic Press, 2017).
85. D. O. Hebb, The Organization of Behavior (Wiley, 1949).
86. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann, 1988).
87. J. Pearl, Causality: Models, Reasoning and Inference (Cambridge University Press, 2009).
88. G. W. Imbens, D. B. Rubin, Causal Inference in Statistics, Social, and Biomedical Sciences (Cambridge University Press, 2015).
89. J. Pearl, Causal inference in statistics: An overview. Stat. Surv. 3 , 96-146 (2009).
90. S. Ma, P. Kemmeren, C. F. Aliferis, A. Statnikov, An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation. Sci. Rep. 6 , 22558 (2016). March 27, 2020 preprint Levenstein et al . -On the role of theory and modeling in neuroscience | 20
91. A. V. Alekseyenko, et al. , Causal graph-based analysis of genome-wide association data in rheumatoid arthritis. Biol. Direct 6 , 25 (2011).
92. S. Mani, C. Aliferis, S. Krishnaswami, T. Kotchen, Learning causal and predictive clinical practice guidelines from data. Stud. Health Technol. Inform. 129 , 850-854 (2007).
93. K. J. Friston, J. Kahan, B. Biswal, A. Razi, A DCM for resting state fMRI. Neuroimage 94 , 396-407 (2014).
94. K. E. Stephan, et al. , Nonlinear dynamic causal models for fMRI. Neuroimage 42 , 649-662 (2008).
95. H. B. Barlow, Others, Possible principles underlying the transformation of sensory messages. Sensory communication 1 , 217-234 (1961).
96. K. P. Kording, J. B. Tenenbaum, R. Shadmehr, The dynamics of memory as a consequence of optimal adaptation to a changing body. Nat. Neurosci. 10 , 779-786 (2007).
97. W. Bialek, Biophysics: Searching for Principles (Princeton University Press, 2012).
98. W. Bialek, S. Setayeshgar, Cooperativity, sensitivity, and noise in biochemical signaling. Phys. Rev. Lett. 100 , 258101 (2008).
99. G. A. Parker, J. M. Smith, Optimality theory in evolutionary biology. Nature 348 , 27-33 (1990).
100. F. Rieke, D. Warland, R. de Ruyter van Steveninck, W. Bialek, Spikes (MIT Press, 1997).
101. G. D. Field, F. Rieke, Nonlinear signal transfer from mouse rods to bipolar cells and implications for visual sensitivity. Neuron 34 , 773-785 (2002).
102. E. Doi, M. Lewicki, Optimal retinal population coding predicts inhomogeneous light adaptation and contrast sensitivity across the visual field. Journal of Vision 14 , 1188-1188 (2014).
103. R. Gregory, P. Cavanagh, The Blind Spot. Scholarpedia J. 6 , 9618 (2011).
104. S. J. Gould, Hen's Teeth and Horse's Toes (Norton, 1983).
105. L. Valiant, Probably Approximately Correct: NatureÕs Algorithms for Learning and Prospering in a Complex World (Basic Books, 2013).
106. A. Wikenheiser, D. W. Stephens, A. D. Redish, Subjective costs drive overly-patient foraging strategies in rats on an intertemporal foraging task. Proc. Natl. Acad. Sci. U. S. A. 110 , 8308-8313 (2013).
107. B. M. Sweis, et al. , Sensitivity to "sunk costs" in mice, rats, and humans. Science 361 , 178-181 (2018).
108. B. Schmidt, A. A. Duin, A. D. Redish, Disrupting the medial prefrontal cortex alters hippocampal sequences during deliberative decision making. J. Neurophysiol. 121 , 1981-2000 (2019).
109. B. M. Sweis, M. J. Thomas, A. D. Redish, Mice learn to avoid regret. PLoS Biol. 16 , e2005853 (2018).
110. H. A. Simon, Theories of bounded rationality. Decision and organization 1 , 161-176 (1972).
111. A. P. Steiner, A. D. Redish, Behavioral and neurophysiological correlates of regret in rat decision-making on a neuroeconomic task. Nat. Neurosci. 17 , 995-1002 (2014).
112. D. H. Hubel, T. N. Wiesel, Brain mechanisms of vision. Sci. Am. 241 , 150-162 (1979).
113. D. H. Hubel, T. N. Wiesel, Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J. Physiol. 160 , 106-154 (1962).
114. M. Carandini, Area V1. Scholarpedia J. 7 , 12105 (2012).
115. L. Paninski, Maximum likelihood estimation of cascade point-process neural encoding models. Network 15 , 243-262 (2004).
116. K. W. Latimer, F. Rieke, J. W. Pillow, Inferring synaptic inputs from spikes with a conductance-based neural encoding model. Elife 8 (2019).
117. B. A. Olshausen, D. J. Field, Sparse coding with an overcomplete basis set: a strategy employed by V1? Vision Res. 37 , 3311-3325 (1997).
118. P. Churchland, T. J. Sejnowski, The computational Brain (MIT Press, 1994).
119. D. Marr, Vision (W. H. Freeman and Co., 1982).
120. G. M. Shepherd, Neurobiology (Oxford University Press, 1994).
121. T. Sejnowski, C. Koch, P. Churchland, Computational neuroscience. Science 241 , 1299-1306 (1988). March 27, 2020 preprint Levenstein et al . -On the role of theory and modeling in neuroscience | 21
122. S. Guttinger, A. C. Love, Characterizing scientific failure: Putting the replication crisis in context. EMBO Rep. 20 , e48765 (2019).
123. T. Colburn, G. Shute, Abstraction in Computer Science. Minds Mach. 17 , 169-184 (2007).
124. J. M. Wing, Computational thinking and thinking about computing. Philos. Trans. A Math. Phys. Eng. Sci. 366 , 3717-3725 (2008).
125. T. O'Leary, A. C. Sutton, E. Marder, Computational models in the age of large datasets. Current Opinion in Neurobiology 32 , 87-94 (2015).
126. M. I. Eronen, D. S. Brooks, Levels of Organization in Biology. The Stanford Encyclopedia of Philosophy (2018).
127. B. B. Machta, R. Chachra, M. K. Transtrum, J. P. Sethna, Parameter space compression underlies emergent theories and predictive models. Science 342 , 604-607 (2013).
128. M. K. Transtrum, et al. , Perspective: Sloppiness and emergent theories in physics, biology, and beyond. J. Chem. Phys. 143 , 010901 (2015).
129. R. N. Gutenkunst, et al. , Universally sloppy parameter sensitivities in systems biology models. PLoS Comput. Biol. 3 , 1871-1878 (2007).
130. A. A. Prinz, D. Bucher, E. Marder, Similar network activity from disparate circuit parameters. Nat. Neurosci. 7 , 1345-1352 (2004).
131. D. Panas, et al. , Sloppiness in spontaneously active neuronal networks. J. Neurosci. 35 , 8480-8492 (2015).
132. H. R. Wilson, J. D. Cowan, A mathematical theory of the functional dynamics of cortical and thalamic tissue. Kybernetik 13 , 55-80 (1973).
133. D. J. Pinto, J. C. Brumberg, D. J. Simons, G. B. Ermentrout, A Quantitative Population Model of Whisker Barrels: Re-examining the Wilson-Cowan Equations. J. Comput. Neurosci. 3 , 247-264 (1996).
134. A. Destexhe, T. J. Sejnowski, The Wilson-Cowan model, 36 years later. Biol. Cybern. 101 , 1-2 (2009).
135. J. Jalics, M. Krupa, H. G. Rotstein, A novel mechanism for mixed-mode oscillations in a neuronal model. Dynamical Systems: An International Journal iFirst , 1-38 (2010).
136. H. G. Rotstein, T. Oppermann, J. A. White, N. Kopell, A reduced model for medial entorhinal cortex stellate cells: subthreshold oscillations, spiking and synchronization. J. Comput. Neurosci. 21 , 271-292 (2006).
137. Z. W. Pylyshyn, Computation and Cognition: Toward a Foundation for Cognitive Science (MIT Press, 1984).
138. C. L. Tan, Z. A. Knight, Regulation of Body Temperature by the Nervous System. Neuron 98 , 31-48 (2018).
139. S. F. Morrison, K. Nakamura, Central neural pathways for thermoregulation. Front. Biosci. 16 , 74-104 (2011).
140. W. Bechtel, Mechanisms in Cognitive Psychology: What Are the Operations? Philos. Sci. 75 , 983-994 (2008).
141. E. Fehr, I. Krajbich, "Social preferences and the brain" in Neuroeconomics (Second Edition) , (Elsevier, 2014), pp. 193-218.
142. M. Gilead, Y. Trope, N. Liberman, Above and Beyond the Concrete: The Diverse Representational Substrates of the Predictive Brain. Behav. Brain Sci. , 1-63 (2019).
143. M. Gilead, N. Liberman, A. Maril, Construing counterfactual worlds: The role of abstraction. European Journal of Social Psychology 42 , 391-397 (2012).
144. L. Wu, D. Wang, J. A. Evans, Large teams develop and small teams disrupt science and technology. Nature 566 , 378-382 (2019).
145. P. Grim, et al. , Scientific Networks on Data Landscapes: Question Difficulty, Epistemic Success, and Convergence. Episteme 10 , 441-464 (2013).
146. T. J. Sejnowski, P. S. Churchland, J. A. Movshon, Putting big data to good use in neuroscience. Nat. Neurosci. 17 , 1440-1441 (2014).
147. W. C. Wimsatt, "Reductionism, Levels of Organization, and the Mind-Body Problem" in Consciousness and the Brain: A Scientific and Philosophical Inquiry , G. G. Globus, G. Maxwell, I. Savodnik, Eds. (Springer US, 1976), pp. 205-267.
148. W. Bechtel, Philosophy of science: An overview for cognitive science (Psychology Press, 2013).
149. Y. Dudai, K. Evers, To simulate or not to simulate: what are the questions? Neuron 84 , 254-261 (2014).
150. Y. Goldman, M. Morad, Ionic membrane conductance during the time course of the cardiac action potential. J. Physiol. March 27, 2020 preprint Levenstein et al . -On the role of theory and modeling in neuroscience | 22 268 , 655-695 (1977).
151. A. M. Katz, Cardiac ion channels. N. Engl. J. Med. 328 , 1244-1251 (1993).
152. G. B. Ermentrout, N. Kopell, Frequency Plateaus in a Chain of Weakly Coupled Oscillators, I. SIAM J. Math. Anal. 15 , 215-237 (1984).
153. P. W. Bridgman, The logic of modem physics. New York (1927).
154. H. Chang, Inventing Temperature: Measurement and Scientific Progress (Oxford University Press, 2007).
155. G. de Hollander, B. U. Forstmann, S. D. Brown, Different Ways of Linking Behavioral and Neural Data via Computational Cognitive Models. Biol Psychiatry Cogn Neurosci Neuroimaging 1 , 101-109 (2016).
156. J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U. S. A. 79 , 2554-2558 (1982).
157. J. J. Hopfield, D. Tank, ``Neural'' computation of decisions in optimization problems. Biol. Cybern. 52 , 141-152 (1985).
158. J. Hertz, A. Krogh, R. G. Palmer, Introduction to the Theory of Neural Computation (Addison-Wesley, 1991).
159. C. A. Del Negro, G. D. Funk, J. L. Feldman, Breathing matters. Nat. Rev. Neurosci. 19 , 351-367 (2018).
160. J.-M. Ramirez, N. Baertsch, Defining the Rhythmogenic Elements of Mammalian Breathing. Physiology 33 , 302-316 (2018).
161. F. Peña, M. A. Parkis, A. K. Tryba, J.-M. Ramirez, Differential contribution of pacemaker properties to the generation of respiratory rhythms during normoxia and hypoxia. Neuron 43 , 105-117 (2004).
162. C. A. Del Negro, et al. , Sodium and calcium current-mediated pacemaker neurons and respiratory rhythm generation. J. Neurosci. 25 , 446-453 (2005).
163. D. Levenstein, G. Buzsáki, J. Rinzel, NREM sleep in the rodent neocortex and hippocampus reflects excitable dynamics. Nat. Commun. 10 , 2478 (2019).
164. P. J. Gonçalves, et al. , Training deep neural density estimators to identify mechanistic models of neural dynamics. bioRxiv , 838383 (2019).
165. S. R. Bittner, et al. , Interrogating theoretical models of neural computation with deep inference. bioRxiv , 837567 (2019).
166. J. Gunawardena, Models in biology: "accurate descriptions of our pathetic thinking." BMC Biol. 12 , 29 (2014).
167. C. Omar, J. Aldrich, R. C. Gerkin, Collaborative infrastructure for test-driven scientific model validation. of the 36th International Conference on … (2014).
168. R. C. Gerkin, R. J. Jarvis, S. M. Crook, Towards systematic, data-driven validation of a collaborative, multi-scale model of Caenorhabditis elegans. Philos. Trans. R. Soc. Lond. B Biol. Sci. 373 (2018).
169. T. Kohonen, Self-Organization and Associative Memory (Springer-Verlag, 1984).
170. T. Kohonen, Content-Addressable Memories (Springer, 1980).
171. G. Lakoff, Women, Fire, and Dangerous Things (University of Chicago Press, 1990).
172. E. Rosch, Prototype classification and logical classification: The two systems. New trends in conceptual representation: Challenges (1983).
173. K. Obermayer, G. G. Blasdel, K. Schulten, Statistical-mechanical analysis of self-organization and pattern formation during the development of visual maps. Phys. Rev. A 45 , 7568-7588 (1992).
174. K. Obermayer, T. J. Sejnowski, Howard Hughes Medical Institute Computational Neurobiology Laboratory Terrence J Sejnowski, T. A. Poggio, Self-organizing Map Formation: Foundations of Neural Computation (MIT Press, 2001).
175. N. V. Swindale, H.-U. Bauer, Application of Kohonen's self-organizing feature map algorithm to cortical maps of orientation and direction preference. Proc. R. Soc. Lond. B Biol. Sci. 265 , 827-838 (1998).
176. N. V. Swindale, How different feature spaces may be represented in cortical maps. Network 15 , 217-242 (2004).
177. E. de Villers-Sidani, M. M. Merzenich, Lifelong plasticity in the rat auditory cortex: basic mechanisms and role of sensory experience. Prog. Brain Res. 191 , 119-131 (2011).
178. M. Nahum, H. Lee, M. M. Merzenich, Principles of neuroplasticity-based rehabilitation. Prog. Brain Res. 207 , 141-171 (2013).
179. D. J. Freedman, M. Riesenhuber, T. Poggio, E. K. Miller, Categorical Representation of Visual Stimuli in the Primate March 27, 2020 preprint Levenstein et al . -On the role of theory and modeling in neuroscience | 23
180. Prefrontal Cortex. Science 291 , 312-316 (2001).
181. D. J. Freedman, M. Riesenhuber, T. Poggio, E. K. Miller, A comparison of primate prefrontal and inferior temporal cortices during visual categorization. J. Neurosci. 23 , 5235-5246 (2003).
182. T. J. Wills, C. Lever, F. Cacucci, N. Burgess, J. O'Keefe, Attractor Dynamics in the Hippocampal Representation of the Local Environment. Science 308 , 873-876 (2005).
183. L. L. Colgin, et al. , Attractor-Map Versus Autoassociation Based Attractor Dynamics in the Hippocampal Network. J. Neurophysiol. (2010).
184. T. Yang, M. N. Shadlen, Probabilistic reasoning by neurons. Nature 447 , 1075-1080 (2007).
185. K. Jezek, E. J. Henriksen, A. Treves, E. I. Moser, M. B. Moser, Theta-paced flickering between place-cell maps in the hippocampus. Nature 478 , 246-249 (2011).
186. E. Kelemen, A. A. Fenton, Coordinating different representations in the hippocampus. Neurobiol. Learn. Mem. 129 , 50-59 (2016).
187. O. Guest, A. E. Martin, How computational modeling can force theory building in psychological science (2020) https:/doi.org/ 10.31234/osf.io/rybh9 .
188. I. van Rooij, G. Baggio, Theory before the test: How to build high-verisimilitude explanatory theories in psychological science (2020).
189. W. Bechtel, Integrating sciences by creating new disciplines: The case of cell biology. Biology and Philosophy 8 , 277-299 (1993).
190. A. D. Redish, Addiction as a computational process gone awry. Science 306 , 1944-1947 (2004).
191. A. D. Redish, S. Jensen, A. Johnson, A unified framework for addiction: vulnerabilities in the decision process. Behav. Brain Sci. 31 , 415-487 (2008).
192. P. R. Montague, R. J. Dolan, K. J. Friston, P. Dayan, Computational psychiatry. Trends Cogn. Sci. 16 , 72-80 (2012).
193. A. D. Redish, J. A. Gordon, Eds., Computational Psychiatry: New Perspectives on Mental Illness (MIT Press, 2016).
194. Q. J. M. Huys, T. V. Maia, M. J. Frank, Computational psychiatry as a bridge from neuroscience to clinical applications. Nat. Neurosci. 19 , 404-413 (2016).
195. B. A. Richards, et al. , A deep learning framework for neuroscience. Nat. Neurosci. 22 , 1761-1770 (2019).
196. A. Gomez-Marin, A. A. Ghazanfar, The Life of Behavior. Neuron 104 , 25-36 (2019).
197. P. Cisek, Resynthesizing behavior through phylogenetic refinement. Atten. Percept. Psychophys. 81 , 2265-2287 (2019).
198. R. Brette, Is coding a relevant metaphor for the brain? Behav. Brain Sci. , 1-44 (2019).
199. R. E. Kass, et al. , Computational Neuroscience: Mathematical and Statistical Perspectives. Annu Rev Stat Appl 5 , 183-214 (2018).
200. B. Devezer, L. G. Nardin, B. Baumgaertner, E. O. Buzbas, Scientific discovery in a model-centric framework: Reproducibility, innovation, and epistemic diversity. PLoS One 14 , e0216125 (2019).